Pavemon: A Gis-Based Pavement Monitoring System Using Large Amounts of Near-Surface Geophysical Sensor Data

Shamsabadi, Salar Shahini; Wang, Ming L.; Birken, Ralf

doi:10.1190/sageep.27-067

Cited by 5 publications

(4 citation statements)

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“…Figure 10 shows an overview of PAVEMON's features and different parts. More details can be found in [22]. Figure 10.…”

Section: Pavemon Applicationmentioning

confidence: 97%

“…This hierarchically constructed application provides a unified solution for control, management, operation and processing of the large-scale sensor data collected by the VOTERS prototype [22]. Since multiple such systems might collect data simultaneously VOTERS has automated the data flow through the system from the data acquisition to where it is automatically processed and placed into the database and on the map.…”

Section: Voters Projectmentioning

confidence: 98%

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Framework and implementation of a continuous network-wide health monitoring system for roadways

Wang¹,

Birken²,

Shamsabadi³

2014

SPIE Proceedings

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According to the 2013 ASCE report card America's infrastructure scores only a D+. There are more than four million miles of roads (grade D) in the U.S. requiring a broad range of maintenance activities. The nation faces a monumental problem of infrastructure management in the scheduling and implementation of maintenance and repair operations, and in the prioritization of expenditures within budgetary constraints. The efficient and effective performance of these operations however is crucial to ensuring roadway safety, preventing catastrophic failures, and promoting economic growth. There is a critical need for technology that can cost-effectively monitor the condition of a network-wide road system and provide accurate, up-to-date information for maintenance activity prioritization.The Versatile Onboard Traffic Embedded Roaming Sensors (VOTERS) project provides a framework and the sensing capability to complement periodical localized inspections to continuous network-wide health monitoring. Research focused on the development of a cost-effective, lightweight package of multi-modal sensor systems compatible with this framework. An innovative software infrastructure is created that collects, processes, and evaluates these large time-lapse multi-modal data streams. A GIS-based control center manages multiple inspection vehicles and the data for further analysis, visualization, and decision making. VOTERS' technology can monitor road conditions at both the surface and sub-surface levels while the vehicle is navigating through daily traffic going about its normal business, thereby allowing for network-wide frequent assessment of roadways. This deterioration process monitoring at unprecedented time and spatial scales provides unique experimental data that can be used to improve life-cycle cost analysis models.

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“…Figure 10 shows an overview of PAVEMON's features and different parts. More details can be found in [22]. Figure 10.…”

Section: Pavemon Applicationmentioning

confidence: 97%

Section: Voters Projectmentioning

confidence: 98%

Framework and implementation of a continuous network-wide health monitoring system for roadways

Wang¹,

Birken²,

Shamsabadi³

2014

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

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“…They are less practical on a large scale as users are reluctant to stop and record the pothole locations. PAVEMON [28] is a GIS web-based pavement monitoring system by VOTERS that uses data from multiple sensors such as accelerometer, microscope, tire pressure sensor, imaging, radar etc. to evaluate different road distress parameters.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors

et al. 2020

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Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones for navigation, smartphone-based road condition assessment has emerged as a promising new approach. In this paper, we propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focuses on classification of three main class labels-smooth road, potholes, and deep transverse cracks. We hypothesize that using features from all three axes of the sensors provides more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results indicate that models trained with features from all axes of the smartphone sensors outperform models that use only one axis. We also observe that the use of neural networks provides a significantly improved data classification. The machine learning approach discussed here can be implemented on a larger scale to monitor roads for defects that present a safety risk to commuters as well as to provide maintenance information to relevant authorities.

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“…These methods include tools such as ground penetration radar, infrared thermography [12], laser scanning [13], image-based [14,15], vibration-based [16][17][18], and acoustic-based [19] methods. As these techniques are not mutually exclusive, more than one technique can be used simultaneously [10,20].…”

Section: Introductionmentioning

confidence: 99%

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

Ramos-Romero

Asensio

Moreno

et al. 2022

Sensors

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The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

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Pavemon: A Gis-Based Pavement Monitoring System Using Large Amounts of Near-Surface Geophysical Sensor Data

Cited by 5 publications

References 1 publication

Framework and implementation of a continuous network-wide health monitoring system for roadways

Framework and implementation of a continuous network-wide health monitoring system for roadways

A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

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